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Heart rate variability (HRV) refers to the variation of the heart rate cycles, which contains information of how the autonomic nerves system regulates the cardiovascular system. HRV is a valuable indicator to diagnose various cardiovascular diseases and predict arrhythmia events. This study is based on the standardized five-minute and ten-minute RR interval series from the open source Electrocardiogram (ECG) database website PhysioNet. Artificial Neural Networks (ANN) are used to distinguish patients with congestive heart failure or atrial fibrillation from normal sinus rhythm utilizing features calculated by time and frequency domains as well as nonlinear analysis. To eliminate redundancy and avoid overfitting, Principal Component Analysis (PCA) is performed to screen for the most efficient features. PCA not only improves the accuracy but also greatly reduces the number of nodes in the ANN model, thus, improves the efficiency. Overall, ANN classifiers achieved an accuracy of 79% for five-minute RR interval series and 84% for that of ten-minute series. The performance of Random Forest (RF) classifier is not as satisfactory. However, its list of most important features indicates nonlinear dynamics may play an important role and provide useful insights to the classification problem.
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